The present invention relates generally to the generation of “events” and ultimately a collection of events, referred to as an “event stream”. Events and event streams, in the context of this disclosure, are applicable whenever large and distributed data records are being managed and are subject to heightened privacy or security. In particular, such systems and methods are applicable to the healthcare field, where federal and state laws restrict data in the interest of patient privacy. Such event streams enable platforms for applications, such as analytics, business intelligence, revenue cycle management, utilization management and quality applications.
Despite rapid growth of innovation in other fields in recent decades, the world of medical information, including patient medical records, billing, referrals, and a host of other information, has enjoyed little to no useful consolidation, reliability, or ease-of-access, leaving medical professionals, hospitals, clinics, and even insurance companies with many issues, such as unreliability of medical information, uncertainty of diagnosis, lack of standard, and a slew of other related problems.
One of the challenges facing those in the medical or related areas is the number of sources of information, the great amount of information from each source, maintenance of data in a HIPAA compliant manner, and consolidation of such information in a manner that renders it meaningful and useful to those in the field in addition to patients. Obviously, this has contributed to increased medical costs and is perhaps largely attributed to the field suffering from an organized solution to better aid the medical professionals, to better aid those requiring more reliable patient history and those requiring more control and access over such information.
The concept of “big data” is already well established in the field of information technology. Big data is a collection of tools, techniques and methodologies used when data sets are large and complex that it becomes difficult or impossible to store, query, analyze or process using current database management and data warehousing tools or traditional data processing applications. The challenge of handling big data include capture, curation, storage, search, sharing, analysis and visualization. The trend to larger data sets is due to the proliferation of data capture devices and the ease of capturing and entering data from a wide variety of sources.
Due to the intrinsic issues prevalent with medical information—where very large amounts of clinical and administrative information are generated and stored as unstructured text and scanned documents, big data platforms and analysis is all but unheard of. However, the inability to leverage the entirety of the data results in considerable value being lost by healthcare providers, insurance companies, and patients. For example, a big data platform could enable solutions utilizing all of the data to optimize accurate risk assessment, population health, and revenue for value-based healthcare organizations. Without such a platform, these value added solutions are less obtainable.
It is therefore apparent that an urgent need exists for a standardized way of treating, understanding and analyzing all data, including but not limited to clinical, administrative, billing, utilization, revenue and self-reported data that enables downstream applications for the medical field. Such a system will increase care efficiency and increase care quality, provide risk optimization, and increase revenue for value-based healthcare organizations. Apixio's Event Stream platform provides a standardized way of treating, understanding and analyzing all of enterprise data to enable downstream applications.